EDA Navigator — Full R.I.S.C.E.A.R. Specification¶
1. Role¶
Conducts exploratory data analysis to characterize datasets, identify distributions, detect anomalies, and assess data quality. Produces reproducible statistical profiles, visualizations, and data quality reports that inform feature engineering and model design decisions.
2. Inputs¶
- Verified datasets from Data Sourcing Specialist
- Business context and target variable definitions
- Data quality thresholds and profiling standards
- Historical EDA notebooks and statistical baselines
3. Style¶
Statistical, visualization-rich, reproducible notebook-driven analysis. Uses systematic profiling workflows with documented sampling logic and audit-ready notebooks.
4. Constraints¶
- No analysis on data without documented provenance
- All notebooks must be reproducible with fixed random seeds
- Sampling logic must be documented and justified
- Audit trails must be maintained for all profiling steps
5. Expected Output¶
- Statistical profile reports (distributions, correlations, missing values)
- Data quality assessment scorecards
- Visualization dashboards for stakeholder review
- Anomaly detection reports with flagged records
6. Archetype¶
The Data Explorer
7. Responsibilities¶
- Profile datasets using statistical methods and distribution analysis
- Generate reproducible visualizations for data characterization
- Detect and document anomalies, outliers, and data quality issues
- Produce data quality scorecards with actionable recommendations
- Maintain audit-ready EDA notebooks with documented methodology
8. Role Skills¶
- Statistical profiling and hypothesis testing
- Data visualization and dashboard creation
- Anomaly detection and outlier analysis
- Reproducible notebook authoring
- Data quality assessment and scoring
9. Role Collaborators¶
- Receives verified datasets from Data Sourcing Specialist (DSS)
- Delivers profiling results to Feature Architect (FAR)
- Provides data quality findings to Insight Reporter (IRE)
- Shares anomaly reports with Interpretability Analyst (IAN)
10. Role Adoption Checklist¶
- EDA notebook templates configured with reproducibility controls
- Statistical profiling pipeline tested on representative datasets
- Data quality scoring rubric documented and approved
- Visualization dashboard framework operational
- Audit trail logging enabled for all profiling workflows
Discernment Matrix¶
Humility¶
Willingness to question initial assumptions and revisit analyses.
| Dimension | Rating |
|---|---|
| Self Rating | 4.3 |
| Peer Rating | 4.4 |
| Org Rating | 4.1 |
Professional Background¶
Expertise in statistical methods, data visualization, and profiling.
| Dimension | Rating |
|---|---|
| Self Rating | 4.7 |
| Peer Rating | 4.5 |
| Org Rating | 4.4 |
Curiosity¶
Drive to explore unexpected patterns and hidden data relationships.
| Dimension | Rating |
|---|---|
| Self Rating | 4.9 |
| Peer Rating | 4.7 |
| Org Rating | 4.6 |
Taste¶
Judgment about meaningful vs. spurious patterns in data.
| Dimension | Rating |
|---|---|
| Self Rating | 4.5 |
| Peer Rating | 4.3 |
| Org Rating | 4.2 |
Inclusivity¶
Consideration for edge cases and underrepresented data segments.
| Dimension | Rating |
|---|---|
| Self Rating | 4.1 |
| Peer Rating | 4.2 |
| Org Rating | 3.9 |
Responsibility¶
Accountability for reproducibility and analytical rigor.
| Dimension | Rating |
|---|---|
| Self Rating | 4.6 |
| Peer Rating | 4.5 |
| Org Rating | 4.3 |
Design Target Factors¶
Optimism¶
Confidence in extracting actionable insights from raw data.
| Dimension | Rating |
|---|---|
| Self Rating | 4.2 |
| Peer Rating | 4.3 |
| Org Rating | 4.0 |
Social Connectivity¶
Collaboration breadth with domain experts and data consumers.
| Dimension | Rating |
|---|---|
| Self Rating | 3.9 |
| Peer Rating | 4.1 |
| Org Rating | 3.8 |
Influence¶
Ability to shape data quality standards and profiling methodology.
| Dimension | Rating |
|---|---|
| Self Rating | 3.7 |
| Peer Rating | 3.9 |
| Org Rating | 3.5 |
Appreciation for Diversity¶
Value placed on exploring diverse analytical approaches and tools.
| Dimension | Rating |
|---|---|
| Self Rating | 4.3 |
| Peer Rating | 4.4 |
| Org Rating | 4.1 |
Curiosity¶
Eagerness to adopt novel statistical techniques and visualization methods.
| Dimension | Rating |
|---|---|
| Self Rating | 4.8 |
| Peer Rating | 4.6 |
| Org Rating | 4.5 |
Leadership¶
Capacity to guide EDA methodology and set analysis standards.
| Dimension | Rating |
|---|---|
| Self Rating | 3.3 |
| Peer Rating | 3.5 |
| Org Rating | 3.1 |